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Articles Tagged with: Prepayment Analytics
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Are You Overpaying for VA Prepay Risk in Ginnie II Pools?

Recent history is showing a persistent (and widening) gap between VA and FHA loan prepayment speeds in Ginnie Mae securities.  

Over the past 33 months, VA 30-year loans are prepaying 40 percent faster than FHA 30-year loans (9.4% CPR for VA vs. Just 6.6% for FHA. VA speeds over this period are ranging from 1.15x to 1.77x FHA speeds. 

This divergence is not incidental. With a median spread between the two of around 230 bps, the difference compounds significantly in modeling cash flow expectations and MBS pricing. 

Why this divergence? 

At least three structural factors contribute to faster VA speeds relative to FHA: 

  1. Borrower Profiles: VA borrowers tend to have higher credit scores (727 average FICO as of Sept 2025) than FHA borrowers (678 average FICO). This makes VA borrowers more likely to refinance quickly when market conditions shift. 
  1. Program Rules: VA’s streamlined refinancing programs are generally more accessible, lowering the cost of refinancing compared to FHA. 
  1. Servicing Practices: The VA loan servicing ecosystem has historically been more efficient, which can accelerate churn relative to FHA pools. 
  1. Larger Loan Size: The average VA loan size is typically larger than the average FHA loan size, making refinancing more impactful for VA borrowers. 

What does this mean for Ginnie II TBA & Custom pools? 

Ginnie II TBAs typically combine both VA and FHA collateral. Most of the loans are FHA, but VA loans still account for a significant share. Because VA loans prepay substantially faster, TBA investors are effectively buying into faster prepayment risk than they would see in a purely FHA pool.  

This risk manifests itself both in the form of shorter duration and more negative convexity exposure. Investors in Ginnie II TBAs may see faster principal return than modeled if VA share is high, especially if the model fails to differentiate between VA and FHA loans, and the additional negative convexity in VA loans will adversely impact OASes, ceteris paribus. 

For investors seeking more tailored exposure, custom Ginnie pools provide a way to isolate or avoid VA prepayment risk. For instance: 

  • FHA-only pools offer slower, more stable prepayment behavior, attractive for investors prioritizing duration stability. 
  • VA-dominant pools may appeal to investors willing to take on higher turnover in exchange for price discounts or optionality in certain market environments. 

Given current market spreads, the differential between VA and FHA speeds is unlikely to narrow materially in the near term. As refinancing incentives fluctuate, VA borrowers will continue to exhibit faster churn than FHA counterparts. 

For Agency portfolio managers and traders, this reality underscores the importance of collateral composition within Ginnie II TBAs. It also highlights the importance of prepayment models capable of recognizing the differences between FHA and VA loans and taking those differences into account when making forecasts. 

Book a demo for RiskSpan’s Edge platform for Agency MBS Traders and Analysts. 

Consumers Under Pressure as Markets Seek Stability: October Models & Markets Recap 

Register here for next month’s call: Thursday, November 20th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call focused on the impact of the Fed rate cut, key macro indicators and a spotlight on the surging second-lien market. 

Here’s a quick recap in case you missed it. 

(Click here for the entire 23-minute recording or continue reading for a summary.)  

Rates Ease, but Headwinds Persist 

October has brought a modest reprieve in mortgage rates, with the 30-year fixed rate having fallen to approximately 6.2%, the lowest level in nearly a year. 

Affordability remains constrained, however, and long-term headwinds appear far from resolved. Specifically: 

Unemployment remains near 4.2%, and core PCE inflation continues to hover around 2.8%. While steady, this remains above the Fed’s comfort zone. 

Home price growth is slowing nationally, with several major metros posting month-over-month declines. 

Fed Funds futures suggest rates will stay elevated into 2026, with year-end 2025 expectations still in the 3.5–3.75% range. 

Together, these indicators suggest a “higher for longer” policy regime even as the market eyes rate cuts later this year. 

HELOC and Second-Lien Insights: Delinquencies on the Rise 

Leveraging the Equifax Analytic Dataset, a 10% sample of active U.S. credit borrowers with anonymized tradeline-level detail, enables us to dive deep into Home Equity Loans (HELs) and Home Equity Lines of Credit (HELOCs). These asset classes are gaining renewed investor attention as homeowners tap existing equity rather than selling into a high-rate market.

Delinquency rates are trending upward for both HELs and HELOCs, particularly among lower-credit-score borrowers. Aggregated five-year views on page 11 highlight the steady climb, with 600-score cohorts showing the sharpest deterioration. 

These findings echo broader signals of consumer strain visible across other loan products. 

Consumer Balance Sheets Under Pressure

The New York Fed’s Q2 2025 Household Debt and Credit Report underscored the strain many consumers face. Total household debt continues to climb, driven by non-housing credit categories—auto loans, student debt, and revolving balances in particular. 

Credit card and auto loan delinquencies have risen sharply, while mortgage and HELOC performance, though still comparatively solid, are trending downward. Even with stable macro indicators, consumers remain financially stretched. This dynamic is likely to influence credit performance and securitization trends into 2026.

Prepayment Model Updates 

Our prepayment models continue to align well with observed speeds across both Conventional and Ginnie collateral. Lower-coupon collateral (WAC ≤ 5.5%) experienced some deceleration versus forecasts—a function of seasonality and slower housing turnover.  

Higher-coupon cohorts (WAC ≥ 6.0%) reflected more volatility, consistent with recent refinance activity at the margins. 

We continue to add additional analytics reports on the platform. Please visit www.riskspan.com/request-access to request free access. 

Also, please feel free to contact us to discuss or learn more.

Use Case: RiskSpan’s Agentic AI for MBS Data Tool 

Breaking Down VA vs FHA Prepayment Speeds 

This use case demonstrates how agentic interaction with the MBS Data Tool helps investors move from a general observation (“VA speeds are fast”) to a data-backed, multidimensional explanation tied directly to credit, LTV, and program structure. 

Business Question 

Why are VA loans — particularly those with low FICO and high LTV profiles — prepaying so much faster than historically, and faster than FHA loans with comparable characteristics? 

For the full output of our AI MBS Agent, read on or watch our two-minute demo video:

Workflow with Agentic AI 

1. Baseline Comparison 

Based on the data, I can now provide a comprehensive analysis of VA vs FHA loan prepayment trends since 2020:

Key Findings:

1. Current Prepayment Speeds (as of September 2025):

– VA loans: 11.74% CPR

– FHA loans: 8.68% CPR

– VA loans are currently prepaying about 3 percentage points faster than FHA loans

2. Historical Trends:

2020-2021 (Refinance Wave):

– Both loan types experienced very high prepayment speeds

– Peak CPRs:

* FHA: 38.48% (April 2021)

* VA: ~30-31% (Late 2021)

– VA loans consistently showed higher prepayment sensitivity during this period

2022-2023 (Rising Rate Environment):

– Both loan types saw significant slowdown in prepayment speeds

– Lowest CPRs:

* FHA: 4.42% (February 2023)

* VA: 5.07% (February 2023)

– The spread between VA and FHA prepayment speeds narrowed but remained positive

2024-2025 (Current Environment):

– Moderate prepayment speeds for both loan types

– VA loans maintain faster prepayment speeds

– Recent range:

* FHA: 5-10% CPR

* VA: 7-14% CPR

3. Key Observations:

– VA loans consistently prepay faster than FHA loans across different rate environments

– The spread between VA and FHA prepayment speeds tends to widen in more favorable refinancing environments

– Both loan types show similar seasonal patterns but with different magnitudes

– VA loans show greater prepayment sensitivity to market conditions

4. Portfolio Sizes:

– FHA: Currently ~7.45 million loans with $1.47 trillion in balance

– VA: Currently ~3.77 million loans with $1.03 trillion in balance

This analysis suggests that investors should expect consistently faster prepayment speeds from VA loans compared to FHA loans, particularly during periods of favorable refinancing opportunities. The higher prepayment sensitivity of VA loans likely reflects the generally better credit quality of VA borrowers and their greater ability to refinance when rates are favorable.

2. High-Credit Borrowers 

Based on the analysis of high-FICO (740+) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-FICO VA: 9.37% CPR

– High-FICO FHA: 5.96% CPR

– Spread: 3.41% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-FICO VA Loans:

– Average FICO: 779

– Average WAC: 3.85%

– Average CLTV: 58.1%

– Balance: $443.4B (43% of VA portfolio)

– Loan Count: 1.47M loans

High-FICO FHA Loans:

– Average FICO: 769

– Average WAC: 4.55%

– Average CLTV: 58.4%

– Balance: $188.0B (13.8% of FHA portfolio)

– Loan Count: 879K loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-FICO VA: 4.26% to 14.76% CPR

– High-FICO FHA: 3.56% to 8.68% CPR

Seasonal Patterns:

– Both show seasonal increases in spring/summer months

– VA loans show more pronounced seasonal effects

– Peak prepayment typically occurs in October/November

4. Notable Observations:

– The prepayment speed differential between VA and FHA remains significant even at high FICO levels

– High-FICO VA loans have consistently lower WACs (about 70bps lower)

– Similar CLTV profiles between high-FICO VA and FHA

– VA has a much larger proportion of high-FICO borrowers (43% vs 13.8% for FHA)

– High-FICO VA loans show greater prepayment sensitivity to market conditions

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– The spread between VA and FHA prepayment speeds has remained relatively stable

– High-FICO VA loans maintain faster prepayment speeds even in rising rate environments

This analysis suggests that even when controlling for credit quality (FICO ≥ 740), VA loans consistently prepay faster than FHA loans. This is likely due to:

1. Lower average note rates (WAC)

2. VA’s no-down-payment benefit making it easier to refinance

3. Generally better borrower demographics and income profiles

4. More streamlined refinancing options for VA loans

Investors should expect continued faster prepayment speeds from high-FICO VA loans compared to high-FICO FHA loans, particularly during favorable rate environments or seasonal peaks.

3. Low-Credit Borrowers 

Based on the analysis of low-FICO (<640) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– Low-FICO VA: 21.04% CPR

– Low-FICO FHA: 12.07% CPR

– Spread: 8.97% faster for VA

2. Portfolio Characteristics (as of September 2025):

Low-FICO VA Loans:

– Average FICO: 611 – Average WAC: 4.77%

– Average CLTV: 66.2%

– Average Age: 52.3 months

– Balance: $77.5B (8.3% of VA portfolio)

– Loan Count: 316K loans

Low-FICO FHA Loans:

– Average FICO: 609

– Average WAC: 4.83%

– Average CLTV: 60.4%

– Average Age: 62.8 months

– Balance: $290.7B (21.3% of FHA portfolio)

– Loan Count: 1.49M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– Low-FICO VA: 9.08% to 27.54% CPR

– Low-FICO FHA: 6.31% to 12.73% CPR

Notable Patterns:

– Much higher volatility in VA prepayment speeds

– Larger seasonal effects in VA loans

– Consistently wider spread between VA and FHA vs high-FICO segment

4. Key Differences:

Portfolio Composition:

– FHA has a much larger concentration in low-FICO loans (21.3% vs 8.3% for VA)

– FHA’s low-FICO portfolio is nearly 4x larger than VA’s by balance

Performance Characteristics:

– VA loans show significantly higher prepayment speeds despite similar credit profiles

– VA loans exhibit greater prepayment volatility

– VA loans have slightly better average FICO scores within the low-FICO segment

– VA loans have higher CLTVs but lower WACs

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds are consistently higher and more volatile

– Seasonal patterns are more pronounced in VA loans

– The spread between VA and FHA prepayment speeds has remained wide

6. Notable Observations:

– The prepayment speed differential between VA and FHA is actually larger in the low-FICO segment than in the high-FICO segment

– Low-FICO VA loans show surprisingly high prepayment speeds despite lower credit scores

– FHA loans show more consistent, less volatile prepayment patterns

– VA loans maintain faster speeds despite higher CLTVs

This analysis reveals that the prepayment behavior difference between VA and FHA loans is even more pronounced in the low-FICO segment than in the high-FICO segment. This could be due to:

1. VA’s stronger refinancing program benefits for lower-credit borrowers

2. More flexible VA streamline refinance requirements

3. Better servicing support for VA borrowers

4. Lower average note rates in the VA program

5. Potentially different borrower demographics within the same FICO band

Investors should expect:

– Higher and more volatile prepayment speeds from low-FICO VA loans

– More stable, lower prepayment speeds from low-FICO FHA loans

– Larger seasonal effects in VA loans

– Greater market sensitivity in VA loans

4. High-LTV Loans 

Based on the analysis of high-LTV (≥95%) VA and FHA loans, here are the key findings:

1. Current Prepayment Speeds (September 2025):

– High-LTV VA: 12.37% CPR

– High-LTV FHA: 7.77% CPR

– Spread: 4.60% faster for VA

2. Portfolio Characteristics (as of September 2025):

High-LTV VA Loans:

– Average FICO: 714

– Average WAC: 4.33%

– Average CLTV: 70.9%

– Average Original LTV: 100.3%

– Average Age: 52.9 months

– Balance: $607.2B (61.1% of VA portfolio)

– Loan Count: 2.11M loans

High-LTV FHA Loans:

– Average FICO: 679

– Average WAC: 4.61%

– Average CLTV: 62.9%

– Average Original LTV: 97.6%

– Average Age: 71.7 months

– Balance: $1.04T (72.0% of FHA portfolio)

– Loan Count: 5.08M loans

3. Key Trends (2024-2025):

Prepayment Speed Range:

– High-LTV VA: 5.60% to 19.73% CPR

– High-LTV FHA: 4.50% to 8.89% CPR

Notable Patterns:

– Higher volatility in VA prepayment speeds

– Strong seasonal patterns in VA prepayments

– More stable FHA prepayment patterns

– Consistent spread between VA and FHA

4. Key Differences:

Portfolio Composition:

– Both programs have significant high-LTV concentration

– FHA has larger absolute exposure but similar relative exposure

– VA allows higher original LTV (100%+) compared to FHA

Performance Characteristics:

– VA loans consistently prepay faster despite higher LTVs

– VA borrowers have notably higher FICO scores (+35 points)

– VA loans have lower WACs (-28bps)

– VA loans are significantly younger (-19 months)

5. Recent Trends:

– Both loan types show increasing prepayment speeds in 2025

– VA prepayment speeds remain more volatile and responsive to seasonality

– Peak prepayment periods occur in spring/summer months

– The spread between VA and FHA has remained relatively stable

6. Notable Observations:

– High-LTV concentration is a defining characteristic of both programs

– VA’s higher prepayment speeds persist despite higher LTVs

– Better credit quality of VA borrowers likely contributes to faster speeds

– Younger average loan age in VA portfolio may influence prepayment behavior

– Lower WACs in VA portfolio suggest more efficient refinancing history

Implications for Investors:

1. Portfolio Management:

– Expect faster and more volatile prepayments from high-LTV VA loans

– Consider seasonal patterns in VA prepayments for timing decisions

– Factor in the significant WAC differential when evaluating prepayment risk

2. Risk Considerations:

– Higher prepayment risk in VA despite similar LTV profiles

– Greater market sensitivity in VA portfolio

– More stable, predictable prepayment patterns in FHA

3. Valuation Impact:

– Need to apply larger prepayment speed differentials for VA vs FHA

– Consider stronger seasonal effects in VA valuations

– Account for credit quality differences within high-LTV segment

The analysis suggests that even in the high-LTV segment, VA loans maintain significantly faster prepayment speeds than FHA loans, driven by:

1. Better borrower credit quality

2. Lower note rates

3. More flexible refinancing options

4. Younger loan age

5. More efficient VA refinancing program

These factors appear to overcome the theoretical constraints of high LTV ratios on refinancing ability.

5. Program Features as Drivers

The data strongly supports that program features drive faster VA prepayment speeds. Here’s the detailed analysis:

1. Early Prepayment Behavior (0-24 months):

– VA loans age 13-24 months: 25.29% CPR

– FHA loans age 13-24 months: 17.04% CPR

– The 8.25% difference reflects VA’s more flexible refinancing rules

– No seasoning requirement for VA vs 210 days for FHA

2. Refinance Incentive Response:

– High incentive (>100bp) VA: 50.43% CPR

– High incentive (>100bp) FHA: 26.55% CPR

– The dramatic difference (23.88%) shows VA borrowers can more easily act on rate incentives

– Even medium incentive (0-100bp) shows large gap:

* VA: 29.59% CPR

* FHA: 18.37% CPR

3. Credit Quality Impact:

– VA average FICO: 715-724 across age buckets

– FHA average FICO: 672-681 across age buckets

– Better VA credit quality + easier program rules = faster speeds

4. Cost Structure Effects:

VA Advantages:

– Lower funding fee (0.5%)

– No ongoing MIP

– Can include closing costs

– Results in lower all-in refinancing costs FHA Constraints:

– Higher upfront MIP (1.75%)

– Ongoing annual MIP

– More restricted closing cost inclusion

– Higher total refinancing costs

5. LTV Considerations:

– VA has no maximum LTV for IRRRL

– FHA generally limited to 97.75% LTV

– Current data shows:

* VA high-LTV loans (>95%): 12.37% CPR

* FHA high-LTV loans (>95%): 7.77% CPR

6. Program Flexibility Evidence:

Response to Rate Environment:

– VA loans show stronger reaction to refi incentives

– Higher CPRs across all incentive buckets

– Faster response to market opportunities

Seasoning Effects:

– VA shows earlier prepayment spikes

– More pronounced early-age prepayment behavior

– Less constrained by seasoning rules

7. Servicer Impact:

– VA servicers may be more proactive due to program ease

– Lower documentation requirements reduce operational friction

– Simpler approval process enables faster execution

The data demonstrates that VA’s program features create a more efficient refinancing mechanism:

1. Lower Costs:

– Reduced fees

– No ongoing MIP

– More included costs

2. Fewer Restrictions:

– No LTV limits

– No seasoning requirement

– Limited credit checks

3. Simpler Process:

– Less documentation

– Faster approval

– Lower operational burden

These advantages manifest in:

– Consistently faster prepayment speeds

– Stronger response to rate incentives

– Earlier refinancing activity

– Higher refinancing success rates

For investors, this means:

1. Expect faster VA prepayments across market cycles

2. Anticipate stronger response to refinancing opportunities

3. Factor in program differences when valuing VA vs FHA MBS

4. Consider seasoning rules when evaluating young loans

The combination of program features and data evidence clearly shows that VA’s more flexible refinancing rules are a primary driver of faster prepayment speeds compared to FHA.

6. Scenario Stress Test 

Based on the current portfolio characteristics and historical refinancing behavior, here’s how a 100bps rate drop would likely impact VA vs FHA prepayment speeds:

1. Current Portfolio Rate Distribution:

VA Portfolio:

– 7.5%+ WAC: $4.6B (0.4%)

– 7.0-7.49% WAC: $18.5B (1.8%)

– 6.5-6.99% WAC: $71.1B (6.9%)

– 6.0-6.49% WAC: $105.3B (10.2%)

– 5.5-5.99% WAC: $118.7B (11.5%)

– <5.5% WAC: $713.0B (69.2%)

FHA Portfolio:

– 7.5%+ WAC: $23.6B (1.6%)

– 7.0-7.49% WAC: $68.5B (4.7%)

– 6.5-6.99% WAC: $163.8B (11.2%)

– 6.0-6.49% WAC: $158.4B (10.8%)

– 5.5-5.99% WAC: $149.0B (10.2%)

– <5.5% WAC: $903.5B (61.5%)

2. Projected Prepayment Speeds by Refi Incentive:

Based on current behavior with similar incentives:

High Incentive (>100bps):

– VA: 50-55% CPR

– FHA: 25-27% CPR

– Differential: ~25-28% faster for VA

Medium Incentive (50-100bps):

– VA: 35-40% CPR

– FHA: 20-25% CPR

– Differential: ~15% faster for VA

Low Incentive (0-50bps):

– VA: 25-30% CPR – FHA: 15-18% CPR

– Differential: ~10-12% faster for VA

3. Portfolio Impact of 100bps Rate Drop:

VA Portfolio:

– ~$200B would move to >100bps incentive

– ~$119B would move to 50-100bps incentive

– Weighted average CPR projection: 32-37% CPR

FHA Portfolio:

– ~$256B would move to >100bps incentive

– ~$149B would move to 50-100bps incentive

– Weighted average CPR projection: 18-22% CPR

4. Key Factors Driving Divergence:

Program Features:

– VA IRRRL has no LTV restrictions

– VA has lower funding fees

– VA has simpler documentation requirements

– VA has no seasoning requirement

Borrower Characteristics:

– VA has higher average FICOs (714 vs 675)

– VA has lower average WACs

– VA has younger average loan age

5. Projected Timeline of Response:

Months 1-3:

– VA speeds increase rapidly to 30-35% CPR

– FHA speeds lag, reaching 15-18% CPR

– Differential: 15-17% CPR

Months 4-6:

– VA peaks at 35-40% CPR – FHA reaches 20-22% CPR

– Differential: 15-18% CPR

Months 7-12:

– VA moderates to 30-35% CPR

– FHA stabilizes at 18-20% CPR

– Differential: 12-15% CPR

6. Risk Factors to Consider:

Upside Risks (faster speeds):

– Pent-up refinance demand

– Improved technology/processing

– Competitive lender environment

Downside Risks (slower speeds):

– Housing market uncertainty

– Credit tightening

– Operational capacity constraints

7. Investment Implications:

1. Premium Pools:

– Expect significantly faster VA prepayments

– Greater price impact on VA premium pools

– Wider VA-FHA OAS spreads

2. Current Coupon Pools:

– Moderate prepayment differential

– Less price impact

– Smaller OAS spread widening

3. Discount Pools:

– Minimal impact on speeds

– Limited price effect

– Stable OAS spreads

Recommendations:

1. Portfolio Positioning:

– Reduce exposure to premium VA pools

– Consider FHA alternatives in premium segment

– Maintain VA exposure in current coupon/discount segments

2. Risk Management:

– Increase prepayment protection in VA holdings

– Consider specified pool stories

– Monitor servicer behavior closely

3. Relative Value:

– Look for opportunities in FHA premium pools

– Consider VA-FHA switches in premium segment

– Evaluate specified pool pay-ups

The analysis suggests a significant divergence in VA vs FHA prepayment speeds following a 100bps rate drop, with VA speeds likely to be 12-25% CPR faster depending on the segment and timing. This differential is driven by both program features and borrower characteristics, with the impact most pronounced in premium pools.

Questions or Need Support? 

Our team is standing by to help you make the most of these new features. Contact us at techsupport@riskspan.com or reach out to your dedicated account manager. 

Interested in a Complimentary Trial or Demo?

Contact us here.

Prepayments Hold Steady, Second Liens Surge: September Models & Markets Recap

Register here for next month’s call: Thursday, October 16th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call focused on the impact of the Fed rate cut, key macro indicators and a spotlight on the surging second-lien market. 

Here’s a quick recap in case you missed it. 

(Click here to listen to the entire 27-minute recording or continue reading for a summary.)  

Market Backdrop: September 2025

Mortgage rates have hit their lowest levels in nearly a year, averaging close to six percent. The Federal Reserve delivered its first rate cut of the current cycle in September 2025, reducing the target range from 400–425 basis points to a projected 350–375 basis points by year-end. Despite this easing, markets continue to anticipate relatively high rates into 2026. 

Inflation and unemployment are holding stable, but long-term headwinds persist, including sluggish real wage growth and affordability constraints in the housing market. Longer-dated Treasury yields are the key driver of mortgage rates, making them essential for investors to keep tabs on. As bond yields set the tone for borrowing costs across the economy, their movement will be critical in shaping both origination volumes and prepayment activity in the coming quarters. 

Spotlight on Second Liens 

The second lien mortgage market continues its emergence as one of the most active areas in structured finance. Issuance and securitization of second lien products have been increasing rapidly, with no signs of slowing. This expansion is driven in part by rising homeowner demand for tapping into the accumulated equity and lenders’ interest in capturing additional credit exposure in a higher-rate environment. 

Prepayment behavior in second lien mortgages, however, differs significantly from that of first liens. This divergence makes specialized model calibration critical. RiskSpan’s Prepayment Models, calibrated against actual second lien performance, indicate that the models are capturing observed dynamics effectively. With issuance expected to continue climbing, accurate modeling of second lien prepayment risk will remain an essential tool for market participants seeking to price and manage these assets. 

Prepayment Model Updates 

Back-testing continues to show that RiskSpan’s prepayment models are tracking well against observed performance across a variety of collateral types. Recent analysis of agency MBS vintages from 2021 and 2022 revealed that higher-coupon pools, particularly those in the 6.5% range, are slowing more than originally anticipated. 

FN/FH 2021-2022 6.5s

By contrast, lower- and mid-coupon pools—those ranging from 1.5% through 5.5%—have remained steady and closely aligned with model expectations. This outcome reinforces the robustness of the models across different coupon bands and provides confidence in their ability to capture nuanced prepayment behavior. 

FN/FH 2021-2022 1.5s – 3.5s

FN/FH 2021-2022 4s – 5.5s

As we introduced during our August call, consumer credit remains a major focus of RiskSpan’s modeling enhancements. Using the Equifax Analytic Dataset, the team has constructed prepayment aging curves for both auto loans and personal loans. These analyses confirm that borrower credit score bands, measured using VantageScore 4.0, influence prepayment behavior in a manner similar to mortgage loans. For auto loans, the score sensitivity is particularly evident across borrower segments. Personal loan data show similar trends, with one notable difference: the effect of loan term is more pronounced after the first year of loan seasoning. This suggests that term structure plays a more significant role in personal loan prepayment decisions compared to auto loans. 

Auto Loan Prepayment Aging Curves

Personal Loan Prepayment Aging Curves

We are in the process of finalizing these consumer loan prepayment models and will release them shortly on the RiskSpan Platform. This will give clients the ability to incorporate a new level of borrower insight into their own portfolio analytics. 

Looking Ahead 

The integration of Equifax ADS into the construction of prepayment aging curves is just the beginning. We continue to expand our modeling capabilities and data integration in order to provide clients with deeper and more actionable insights. Credit card and student loan models are already in the pipeline, and their release will extend RiskSpan’s modeling coverage across the full spectrum of consumer credit products. 

In addition, the team is adding new analytics reports to the Platform, giving clients free access to timely updates and market intelligence. These ongoing enhancements underscore our commitment to equipping the investment management community with the tools and data needed to navigate complex and evolving credit markets. 

Contact us to discuss or learn more.

Higher Rates, Smarter Models, and Fresher Credit Insights: August Models & Markets Recap

Register here for next month’s call: Thursday, September 18th, 2025, 1 p.m. ET. 

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month’s call was a wide-ranging update on new model developments, consumer credit insights, and macroeconomic trends shaping structured finance. 

Here’s a quick recap in case you missed it. 

(Click here to listen to the entire 30-minute recording or continue reading for a summary.)  

Market Outlook: August 2025

Stable employment and inflation notwithstanding, the macro backdrop remains dominated by persistent headwinds: 

  • Mortgage Rates: Still above 6.5% and expected to stay above 6% for the next several years. 
  • Home Prices: Case-Shiller data shows relative stability, with modest month-over-month declines and low year-over-year growth. 
  • Labor & Inflation: Both unemployment and PCE inflation are holding steady. 
  • Fed Policy: The Fed Funds Rate remains in the 4.25%–4.50% range, with the first cut expected in September 2025. Markets anticipate a year-end rate of 3.75%–4.00%, but long-term rates remain elevated. 
  • 10Yr rates unlikely to see a significant decline over next few years, leading to a high mortgage rate environment (>~ 6%) for next 3-5 years. 

New Equifax Data Integration 

We introduced our latest research leveraging the Equifax Analytic Dataset (ADS), a borrower-level anonymized sample representing 10% of the U.S. active credit population. Using tradeline-level detail (credit scores, balances, payments, etc.), we have constructed aging curves for auto loans and personal loans segmented by credit score bands. 

Some key takeaways: 

  • Auto Loan Defaults: Clear segmentation appears across credit score bands, with default curves validated against Federal Reserve data. 

  • Personal Loan Defaults: Similar segmentation trends, with early results indicating significant variation across risk tiers. 
  • Credit card and student loan performance curves: Coming soon. 

The final versions of these datasets will be accessible directly within the RiskSpan platform, allowing clients to benchmark their portfolios against robust national trends. 

Model Updates 

Prepayment Models (Versions 3.2 & 3.7) 

Our prepayment models continue to perform strongly against observed market behavior. The latest back-testing of agency cohorts (Fannie Mae and Freddie Mac 2021/2022 vintages across 1.5%–6.5% coupons) shows that speeds remain broadly consistent with expectations. However, higher coupon pools have recently exhibited slower-than-expected speeds, reflecting both tighter refinancing conditions and borrower credit constraints. 

1.5 to 3.5 Coupons 

6.5 Coupons 

Credit Model 7.0 

Our much-anticipated Credit Model v7 is now available in production on the RiskSpan Platform. Key features include: 

  • Delinquency Transition Matrix – A granular 3-D framework tracking monthly movement of loans through delinquency buckets (30D, 60D, 90D, 120D, 150D, 180D+, Foreclosure, REO). 
  • Severity & Liquidation Enhancements – Expanded severity vectors and a liquidation timeline module allow for more nuanced control of loss projections. 
  • Integration with MSR Engine – Provides detailed P&I and T&I cash flow accounting that captures probabilistic delinquency transitions. 

These enhancements equip investors and risk managers with deeper tools for analyzing loss dynamics across mortgage, GSE, FHA, and VA loan cohorts. 

Contact us to learn more.

Navigating Headwinds with Data and AI: July Models & Markets Recap

Register here for next month’s call: Thursday, August 21st, 2025, 1 p.m.

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month, as interest rates remain elevated and economic uncertainty persists, we addressed how both conventional and AI-based modeling techniques are shaping decision-making processes across agency, non-QM, and ARM products.

Here’s a quick recap in case you missed it.

(Click here to listen to the entire 30-minute recording, or continue reading for a summary.)

Model Performance: Prepayment Dynamics in Focus

RiskSpan’s prepayment model continues to perform well based on benchmarking against actuals across coupon stacks. The team noted:

  • Speeds in higher coupons have slowed relative to expectations, in line with broader refinancing trends as mortgage rates remain high.
  • RiskSpan’s Non-QM Prepayment Model (v3.11) shows strong back-testing performance. While most vintages perform as expected, the 2022 vintage diverged, potentially due to ambiguous underwriting guidelines in QM loans that may have led to adverse selection in the Non-QM space. One possible reason is that this reflects borrower composition differences not captured by traditional metrics.

New ARM Model Launch

An enhanced ARM Prepayment Model (v3.8) is now live in production. It exhibits refined sensitivity to rate shocks and aims to provide improved accuracy for adjustable-rate portfolios in today’s volatile environment.

Claude the Research Assistant: AI in Action

One of the highlights of the call was a deep dive into how we are testing Claude (Anthropic’s well-known LLM) as a mortgage research assistant.

Using a dataset from RiskSpan’s Snowflake instance, Claude orchestrated an end-to-end analytical workflow, including:

  • Retrieving and aggregating partially pre-aggregated loan-level data
  • Generating Python code for analysis and visualization
  • Annotating charts and analyzing prepayment trends

Key Insights from Claude’s Analysis

Claude surfaced several noteworthy trends:

  • FICO Score Sensitivity: Higher credit score bands (>750) showed dramatically higher prepayment rates than lower bands (<650), highlighting the refinancing advantage for more creditworthy borrowers.
  • Loan Size Effect: A positive correlation (0.22) between loan size and prepayment rates suggests that larger loan holders are more motivated to refinance.
  • Mortgage Vintage: Newer vintages (especially 2015–2016) demonstrated greater prepayment sensitivity, likely due to looser underwriting and seasoning effects.
  • Interest Rate Sensitivity: Claude captured the sharp inverse relationship between rates and prepayment, particularly the COVID-era spike and the post-2022 slowdown.

Claude correctly reasoned with the provided data but could not identify some features (like “Spread at Origination”). This raises interesting questions about LLMs’ capacity to reason beyond their training corpus.

Market Outlook: Economic Signals Turning Cautionary

The macro backdrop continues to weigh on securitization and borrower behavior. Highlights from July’s indicators:

  • Mortgage Rates: Remain above 6.5%, with little sign of easing before the Fed’s expected first rate cut in September.
  • Fed Funds Rate: Currently 4.25–4.50%, with year-end projections settling around 3.75–4.00%.
  • Home Prices: Showing stability with little YoY movement in the Case-Shiller Index.
  • Labor and Inflation: Both unemployment and PCE inflation measures remain steady, but signs of economic headwinds are beginning to appear.

On the Horizon

  • RiskSpan’s new credit model (v7), which includes a new delinquency transition matrix, is on track for release by the end of the month.
  • Continued enhancements are being made to the Platform, including new prepayment and performance visualizations for private credit and agency MBS sectors.

Contact us to learn more.

June 2025 Models & Markets Update – Predictive Power Amid Economic Uncertainty

Register here for next month’s call: Thursday, July 17th, 2025, 1 p.m.

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month, we showcased our responsiveness to shifting macroeconomic dynamics and introduced new transparency elements (i.e., back-testing tools) to our prepayment and credit modeling.

Click here to listen to the entire 23-minute recording, or continue reading for a summary.

Agency Prepay Model: Back-testing and Enhanced Control

We are launching a new loan-level prepayment back-testing tool using nearly all agency loans (FN/FH/GN) aged 10 years or less. The tool runs every month through our models with historical home prices and interest rates. Based on this data, we have an interactive dashboard that will allow users to drill down into model performance with far more granularity than currently possible.

Key Enhancements to Prepay Model v. 3.8

A soon to be released version of the prepay model will include:

  • User-defined slope multipliers for both Out-of-the-Money (OTM) and In-the-Money (ITM) performance, offering finer control over refinance sensitivity and turnover behavior.
  • Independent knob control across CONV 30, CONV 15, FHA, and VA loan types.

A redesigned ARM prepayment framework, derived from the fixed-rate model. The new ARM component includes:

  • A realistic payment shock element that aligns prepayment spikes with rate reset events.
  • Improved seasonality and aging ramp that reflects empirical loan behavior

These updates give users the ability to more precisely tune model responses under a variety of macroeconomic and borrower scenarios.

Credit Model: V7 and Delinquency Transitions

The delinquency transition matrix incorporated into our new Credit Model V7 provides users a more nuanced credit risk assessment. This model works in conjunction with the enhanced prepayment model to better simulate the joint dynamics of default and prepay behavior across economic cycles.

Macroeconomic Context: Rates and Risk in a Holding Pattern

We remain cautious in our outlook for the remainder of 2025 and into 2026:

The Fed Funds Rate is expected to remain elevated—currently in the 4.25–4.50% range—with the first rate cut likely in September. By year-end 2025, the market expects it to settle around 3.75–4.00%.

Mortgage rates remain stubbornly high, hovering above 6.5%, putting pressure on origination volumes and reinforcing the value of accurate prepayment modeling.

Home prices and broader macro indicators like unemployment and PCE inflation remain stable, suggesting a “wait-and-see” mode for both consumers and investors.

What’s Next: More Models, More Tools, More Insights

We continue to expand our Platform with new analytics, model documentation, and client-facing tools. Users can soon access the new back-testing report directly within the Platform, alongside these updated prepayment and credit models. These developments reflect our commitment to model transparency, data-driven innovation, and practical tools for real-time market adaptation.

Contact us to learn more.

Models & Markets Update – May 2025

Register here for next month’s call: Friday, June 20th, 2025 (pushed back one day on account of Juneteenth).

Each month, we host a Models & Markets call to offer our insights into recent model performance, emerging credit risks, and broader economic indicators. This month, we spotlighted model backtesting updates, macroeconomic conditions, and market analytics that are shaping investment strategies across loans, securities, and private credit.

Click here to listen to the entire 23-minute recording, or continue reading for a summary.

Prepayment Model Performance and Enhancements

RiskSpan’s prepayment model continues to exhibit strong alignment with observed data across all coupon cohorts. During the call, we showcased updated backtesting results for 2022 FN/FH cohorts across multiple coupon bands (1.5s–3.5s, 4.0s–5.5s, and 6.5s), revealing that projected vs. actual CPRs remain closely correlated, even in volatile rate environments.

Additionally, RiskSpan has introduced a Non-QM-specific prepayment model to address behavioral differences in this segment. This is particularly timely, given elevated delinquency trends discussed later in the session.

Our recently enhanced Credit Model 7, leveraging a delinquency transition matrix, is expected to be released by the end of May and will provide a more granular view of credit migration patterns.

Spread at Origination: A Key Risk Signal

Spread at Origination (SatO), the difference between the borrower’s rate and the prevailing PMMS rate at application, is emerging as a critical predictor of refinance activity. Higher SatO values suppress prepayments even in pools with favorable coupons.

Using MBS loan-level data, we illustrated how SatO dynamics impact investor vs. owner-occupied loans, with notable geographic variation. States like CA, FL, and NY show materially different average rates for investor loans, independent of LLPA effects.

As a forward-looking initiative, we are developing a generalized spread model that isolates residual pricing differences not explained by known borrower or loan characteristics. This could further enhance predictive power by benchmarking loans against peer cohorts defined by origination date, FICO, occupancy, and geography.

Macroeconomic Outlook: Sticky Rates and Stable Housing

The economic backdrop remains mixed:

  • Mortgage rates hover around 6.95%, with no near-term relief in sight.
  • The Fed Funds Rate is projected to stay elevated, with the first potential cut not expected until September 2025. Even then, consensus suggests only a modest decline to 3.75–4.00% by year-end.
  • Home prices are largely stable, as reflected in the Case-Shiller Index. Year-over-year appreciation remains positive but muted.
  • Unemployment stands at 4.0%, and inflation is moderating but still above target.

This persistent high-rate environment will continue to dampen refinance activity and challenge affordability, reinforcing the importance of modeling spread-driven behavior accurately.

Non-QM Delinquencies Spike

The bad news: Delinquencies are surging within the Non-QM sector, particularly for 2022–2023 vintages:

  • DSCR/investor loans are showing delinquency rates an order of magnitude higher than conventional loans.
  • This reinforces the need for robust credit modeling, especially in the private credit space where standard agency risk buffers don’t apply.

The good news: RiskSpan’s new NonQM credit and prepay models are now live to support more accurate surveillance of these exposures.

Contact us to learn more or to request a free demo of our platform and models.

Mounting Pressure in Non-QM Credit: What March 2025 Data Signals for Risk Managers

This is a monthly update on non-QM delinquency rate and roll rate trends based on the March 2025 remittance data. Similar to last month’s post, I use the CoreLogic Non-Agency loan data to split out the Non-QM population by loan type. I compare the relative delinquency performance of mortgages backed by Investor properties vs. loans with full documentation vs. other Non-QM loan types (this last bucket comprises mainly Bank Statement loans). I use a slightly revised and more inclusive definition of Non-QM this month so the overall balance figures are higher and delinquency rates and roll rates are slightly lower than those reported in last month’s post.

The first chart shows that the non-performing delinquency rate (60+ dpd loans as a percentage of the overall population) has risen from a post-COVID low of 0.85% in July 2022 to 3.09% as for the most recent remit month. This increase has been driven by deterioration in the credit performance across all Non-QM loan types. Notably, the delinquency rate for Investor loans increased to 3.56% as of March, up more than three-fold from post-COVID lows of 1.1% in October 2022 and up 91bp year over year. Full Doc Non-QM loans continue to outperform other segments significantly, but their delinquency rates still rose to 0.85%, a new post COVID recovery high.

The other driver of the increase in delinquency rates for the Aggregate Non-QM loan population is a gradual shift in their mix away from the Full Doc loans, which have a better credit profile. As shown in the graph below, Full Doc loans as a percentage of the overall NQM mix have fallen from over 50% of NQM population as of the end of 2018 to only just under 31% in March. Meanwhile, Investor loans have increased from only 3% of the Non-QM population as of the end of 2018 to 10% just before COVID to over 24% as of March.

The last graph considers the gateway transition of mortgages to non-performing status: the current to 30 roll rates, or the percentage of current loans that roll to 30 days delinquent in any given month. These trends are broadly in line with what we see for the overall delinquency rates: roll rates have increased significantly since their post COVID lows.

In the March remittance data, overall Non-QM C->30 roll rates increased to 1.18%, their highest level since December 2020. All 3 non-QM segments broken out in this graph also hit new post COVID highs, with Investor-backed loans experiencing a 1.43% C->30 roll rate, 2.9x the 0.50% roll rate experienced by Full Doc Non-QM loans.

As non-QM mortgages show signs of growing distress amid broader economic uncertainty, we recommend heightened vigilance for investors and risk managers with Non-QM exposure in their portfolios. RiskSpan’s credit models forecast delinquency roll rates directly, and our modeling team calibrates our suite of models to capture both the overall trends and the differentiated performance across loan and product types. These models are just one component of our scaled analytics solutions to help our clients evaluate risk and make investment decisions.

Contact me to discuss.

RiskSpan’s April 2025 Models & Market Call: Credit Model v7, Prepay Volatility, and Credit Trends to Watch

Register here for our next monthly model update call: Thursday, May 15th at 1:00 ET.

Note: This post contains highlights from our April 2025 monthly modeling call, which delivered insights into the current economic climate, mortgage model enhancements, and borrower behavior trends. You can register here to watch a recording of the full 28-minute call.

Here’s what you missed:

Market Overview: A Climate of Volatility

With mortgage rates rebounding to 7%, the panel began by acknowledging the choppy waters ahead, flagging 2025 as a year likely to see persistent rate volatility. As recession risks grow and consumer stress indicators rise, modeling accuracy becomes more important than ever.

Notably, consumers are already strained:

  • Rising consumer debt burdens
  • Increased use of personal loans and second liens for debt consolidation
  • Spikes in HEL/HELOC originations and securitizations
  • Climbing Non-QM delinquencies, particularly among 2022–2023 vintages

Model Update: Credit Model v. 7.0

RiskSpan’s newly released Credit Model v7 marks a significant upgrade in loan performance modeling:

  • Delinquency Transition Matrix core structure
  • The model projects:
    • Monthly CDR, CPR, and delinquency balances (0 through REO)
    • Loss severities, liquidated balances, and P&I flows
  • Modular components include:
    • State Transition Model
    • Severity and Liquidation Timeline Modules
  • The model is fully integrated within RiskSpan’s platform, enabling custom inputs for whole loans and securities

This model empowers users with granular delinquency and cash flow forecasting, critical for managing portfolios amid market uncertainty.

Key findings here included:

  • Daily prepay data showing extreme volatility, but offering early trend visibility
  • Trend lines derived from daily data offering good proxies for future behavior
  • Notable discrepancies within MBS-level data, especially among higher-coupon pools

RiskSpan’s continued focus on benchmarking these data sources helps refine both near-term and long-term modeling strategies.

Prepayment Behavior of Top-Tier Borrowers

The panel spotlighted borrowers with FICO scores over 800, revealing some counterintuitive dynamics:

  • Initial refinance activity is higher in the 800+ cohort—”fastest out of the gate”
  • But post-seasoning, refinance rates fall below those of the 700–750 FICO group
  • This “crossover pattern” reflects a phenomenon the team called “Accelerated Burnout”
  • Assumed strategic behavior, like exploiting lender credits, may amplify early refinance intensity

These insights underscore the nonlinear and evolving nature of borrower behavior, especially under fluctuating rate environments.

Model Performance: Staying on Track

RiskSpan’s Prepayment Model continues to track closely with actuals, validating its calibration even in today’s turbulent landscape. Combined with Credit Model v7, clients now have powerful tools for capturing credit and prepayment risk with more accuracy than ever.

Be sure to register for next month’s model update call on Thursday, May 15th at 1:00 ET.

Want a deeper dive into the new Credit Model or Prepay insights? Contact me to schedule a session with our modeling experts.

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